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The CIO’s Guide to Artificial Intelligence

CIOs can separate AI hype from reality by considering these areas of risk and opportunity.

A chatbot that answers consumers’ questions and directs them to the appropriate place or person is a common example of artificial intelligence (AI), and one most people have experienced personally. But it’s just one way to apply AI technologies.

AI can predict when a key sensor in a machine needs to be replaced to avoid a manufacturing line shutdown or can be used in emergency braking systems to prevent robots from significantly damaging their own components. It can forecast when units will sell out, highlight and respond to patterns in supply chains, and even identify risk factors in investments based on a business’s loan repayment behavior and credit usage.

Common definitions of AI focus on automation and, as a result, often fail to make clear the opportunities available to IT and business leaders

AI-powered applications can assist healthcare providers with diagnosis and search images for early cancer detection. The technology can find key factual law passages and pinpoint how lawyers have used them in other cases; dissect how certain judges think, write and rule; and assist in mediation. It even knows when to change character voices while reading a children’s book.

“Look at how you are using technology today during critical interactions with customers — business moments — and consider how the value of those moments could be increased,” says Whit Andrews, distinguished vice president analyst at Gartner. “Then apply AI to those points for additional business value.”

The basics of artificial intelligence

Gartner defines AI as applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.

Common definitions of AI focus on automation and, as a result, often fail to make clear the opportunities available to IT and business leaders. AI is technology that emulates human performance, typically by learning from it.

Look for critical business points where human interaction or human expertise adds value

The most common mistake with AI is to focus on automation rather than augmentation of human decision making and interactions. If CIOs focus only on further automation via AI, they miss the hidden opportunities for greater personalization and differentiation. AI can augment humans, as it can classify information and make predictions faster and at higher volumes than humans can accomplish on their own.

CIOs should look for critical business points where human interaction or human expertise adds value. They should find examples where such value is manifested in very large amounts of data, especially where the data includes the outcomes that they desire to affect — where customer interactions record whether the customer’s experience was positive, whether a purchaser added an item to a cart, or whether a brake disc was revealed to be worn the predicted amount. They then should consider how AI might augment those efforts to create even more value.

Assess AI maturity

Typically, AI is used to enhance existing applications and processes. For example, it might automate decisions or classify complex data. Both of these examples would traditionally require human intervention and, consequently, increased costs. But AI enables the enterprise to accelerate the process.

To establish a strategy, measure your organization against the AI maturity model. This model can be used as a framework to identify where your organization is on the potential growth curve, communicate with management and decide what steps need to be taken. No matter where your organization is on the map and how far it has to go, ensure that strategies are highly adaptive, with ample room for experimentation.

Pick a spot on the AI model

AI is complicated, and many enterprises are still figuring out how to implement and gain value from the technology. Organizations can fall anywhere on the maturity model, with most currently in the awareness phase and a handful in the transformational phase.

Awareness: Conversations about AI are happening, but not in a strategic way, and no pilot projects or experiments are taking place.

Active: AI is appearing in proofs of concept and possibly pilot projects. Meetings about AI focus on knowledge sharing and the beginnings of standardization conversations.

Operational: At least one AI project has moved to production and best practices, and experts and technology are accessible to the enterprise. AI has an executive sponsor and a dedicated budget.

Systematic: All new digital projects at least consider AI, and new products and services have embedded AI. Employees in process and application design understand the technology. AI-powered applications interact productively within the organization and across the business ecosystem.

Overcome the AI obstacles

When asked about top barriers to AI, enterprises cited finding use cases and defining strategy, security/privacy, risks and integration complexity. Nearly two of three organizations cited finding a starting point as a concern.

This plays out further when considering expected AI project timelines versus actual project timelines. Most organizations start an AI project with a plan to launch the project within two years. However, organizations past the initial planning process estimate it will take four years.

Organizations need to set realistic timelines for AI projects and ensure the desire to push forward with a popular technology doesn’t overrule realistic drawbacks and planning. The hype itself can be a problem, alongside other logistical and strategic challenges.

AI projects face unique obstacles due to their scope and popularity, misperceptions about their value, the nature of the data they touch

Further, it is difficult to determine an AI project’s ROI because most organizations are too early in the process to see any return. Most ROI will be seen in cost reduction and efficiency, as that’s how AI is currently used. However, as enterprises evolve their AI expectations and projects, the technology will mature to have more transformative and strategic impacts.

“AI projects face unique obstacles due to their scope and popularity, misperceptions about their value, the nature of the data they touch and cultural concerns,” says Andrews. “To surmount these hurdles, CIOs should set realistic expectations, identify suitable use cases and create new organizational structures.”

This article has been updated from the original, published on January 2, 2018, to reflect new events, conditions or research.